1 citations · 1 across the 6 of their papers we have counts for
10 papers
SpotEdit: Selective Region Editing in Diffusion Transformers
Zhibin Qin, Zhenxiong Tan, Zeqing Wang +2
Diffusion Transformer models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modi…
FreeSwim: Revisiting Sliding-Window Attention Mechanisms for Training-Free Ultra-High-Resolution Video Generation
Yunfeng Wu, Jiayi Song, Zhenxiong Tan +2
The quadratic time and memory complexity of the attention mechanism in modern Transformer based video generators makes end-to-end training for ultra high resolution videos prohibit…
Vision Bridge Transformer at Scale
Zhenxiong Tan, Zeqing Wang, Xingyi Yang +2
We introduce Vision Bridge Transformer (ViBT), a large-scale instantiation of Brownian Bridge Models designed for conditional generation. Unlike traditional diffusion models that t…
Image Editing As Programs with Diffusion Models
Yujia Hu, Songhua Liu, Zhenxiong Tan +2
While diffusion models have achieved remarkable success in text-to-image generation, they encounter significant challenges with instruction-driven image editing. Our research highl…
Minute-Long Videos with Dual Parallelisms
Zeqing Wang, Bowen Zheng, Xingyi Yang +3
Diffusion Transformer (DiT)-based video diffusion models generate high-quality videos at scale but incur prohibitive processing latency and memory costs for long videos. To address…
Ultra-Resolution Adaptation with Ease
Ruonan Yu, Songhua Liu, Zhenxiong Tan +1
Text-to-image diffusion models have achieved remarkable progress in recent years. However, training models for high-resolution image generation remains challenging, particularly wh…